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Introduction

Emotion recognition plays a crucial role in human communication and interaction. Being able to accurately detect and interpret emotions from speech can have a wide range of applications, from improving customer service to developing more effective human-computer interfaces. In recent years, research in the field of affective computing has focused on developing automated systems that can recognize and respond to human emotions. This thesis aims to contribute to this field by building an emotion recognition system from speech.

Chapter 1: Introduction
1.1 Introduction
1.2 Background of study
1.3 Problem Statement
1.4 Objective of study
1.5 Limitation of study
1.6 Scope of study
1.7 Significance of study
1.8 Structure of the Thesis
1.9 Definition of Terms

Chapter 2: Literature Review
2.1 Introduction to Emotion Recognition
2.2 Speech Processing Techniques
2.3 Emotion Recognition from Speech
2.4 Machine Learning Approaches
2.5 Deep Learning Techniques
2.6 Datasets for Emotion Recognition
2.7 Evaluation Metrics
2.8 Challenges and Limitations
2.9 Trends and Future Directions
2.10 Summary

Chapter 3: System Design and Methodology
3.1 System Architecture
3.2 Data Collection and Preprocessing
3.3 Feature Extraction
3.4 Model Selection
3.5 Training and Testing
3.6 Hyperparameter Tuning
3.7 Cross-validation
3.8 Performance Evaluation
3.9 Ethical Considerations
3.10 Summary

Chapter 4: System Implementation
4.1 Implementation Environment
4.2 Development of the Emotion Recognition System
4.3 Integration with Existing Systems
4.4 Testing and Validation
4.5 Performance Optimization
4.6 User Interface Design
4.7 Deployment and Maintenance
4.8 Results and Discussion
4.9 Comparison with Existing Systems
4.10 Summary

Chapter 5: Conclusion and Summary
5.1 Summary of Findings
5.2 Contributions to the Field
5.3 Implications for Future Research
5.4 Practical Applications
5.5 Limitations and Recommendations for Improvement
5.6 Conclusion

Thesis Overview

Emotion recognition from speech is a challenging task that requires the integration of various disciplines such as signal processing, machine learning, and psychology. This thesis aims to build an emotion recognition system from speech using state-of-the-art techniques in speech processing and machine learning. The research will involve collecting a large dataset of audio recordings containing emotional speech, preprocessing the data, extracting relevant features, training and testing different machine learning models, and evaluating the performance of the system. The findings of this research will contribute to the field of affective computing and have potential applications in diverse areas such as healthcare, education, and entertainment.

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